a) Field of the invention
The present invention relates to a method and apparatus for discriminating a time series data, particularly and, relates to a method and apparatus for discriminating whether the time series data is based on a determinism (,e.g., deterministic signal) or based on a stochastic process (,e.g., random signal).
b) Description of the Related Art
For example, in a rotary mechanical system, when a shaft vibration is abnormal, the observed time series data is synthesized with those based on the determinism and with those based on a stochastic process such as random noises.
It is frequent that irregular-looking time series data may be caused by determining dynamics, and also well known that it is called deterministic chaos. Nowadays, even if the time series data observed from a system has a little noise, it is not always easy, by eye, to recognize whether or not it has some noise. To solve this issue, in general, there is a method of extracting some characteristic frequency by FFT (Fast Fourier Transformation) analysis. But chaotic time series is composed of an infinite number of frequency elements, and give rise to a broad continuous power spectrum.
The characteristic determining method described above using the FFT analyzing method will briefly be described below.
That is to say, at a first step, the time series data observed from the rotary mechanical system is derived from an observer.
At a second step, the observed time series data are analyzed using the FFT analyzer.
At a third step, from the result of spectrum analysis using the FFT analyzer, a characteristic frequency is selected.
At a fourth step, the selected characteristic frequency value is compared with an analyzed value of a normal data which is previously spectrum analyzed using the FFT analyzer.
Finally, the spectrum analyzer determines whether the selected value of the characteristic frequency at the third step is normal or abnormal according to the result of the comparison at the fourth step.
A U.S. Pat. No. 5,576,632 issued on Nov. 19, 1997 exemplifies the FFT analysis for a measurement for a motor current.